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Udemy - Time Series Analysis and Forecasting with Python

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Added 4 years ago by freecoursewb in Other
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Udemy - Time Series Analysis and Forecasting with Python (Size: 4 GB)
  1. Course Content-en_US.srt 4.9 KB
  1. Course Content.mp4 14.7 MB
  1. Create a Datetime with Pandas-en_US.srt 9 KB
  1. Create a Datetime with Pandas.mp4 42.2 MB
  1. Introduction to ARIMA-en_US.srt 21.9 KB
  1. Introduction to ARIMA.mp4 62.4 MB
  1. Introduction to Deep Learning - Basic Concepts-en_US.srt 6.2 KB
  1. Introduction to Deep Learning - Basic Concepts.mp4 25.9 MB
  1. Notice!!-en_US.srt 1.3 KB
  1. Notice!!.mp4 2.6 MB
  10. Development of Univariate LSTM Model 4-en_US.srt 35.3 KB
  10. Development of Univariate LSTM Model 4.mp4 223.5 MB
  10. Pandas 3-en_US.srt 15.9 KB
  10. Pandas 3.mp4 117.8 MB
  11. Development of Univariate LSTM Model 5-en_US.srt 4.8 KB
  11. Development of Univariate LSTM Model 5.mp4 10.1 MB
  11. Pandas 4-en_US.srt 25.6 KB
  11. Pandas 4.mp4 203 MB
  12. Development of Univariate LSTM Model 6-en_US.srt 26.5 KB
  12. Development of Univariate LSTM Model 6.mp4 190.7 MB
  12. Matplotlib 1-en_US.srt 15.4 KB
  12. Matplotlib 1.mp4 99.4 MB
  13. Development of Multivariate LSTM Model 1-en_US.srt 25 KB
  13. Development of Multivariate LSTM Model 1.mp4 111.6 MB
  13. Matplotlib 2-en_US.srt 24.6 KB
  13. Matplotlib 2.mp4 205.2 MB
  14. Development of Multivariate LSTM Model 2-en_US.srt 17.2 KB
  14. Development of Multivariate LSTM Model 2.mp4 138.3 MB
  14. Matplotlib 3-en_US.srt 18.8 KB
  14. Matplotlib 3.mp4 188.8 MB
  15. Development of Multivariate LSTM Model 3-en_US.srt 5.2 KB
  15. Development of Multivariate LSTM Model 3.mp4 11 MB
  15. Matplotlib 4-en_US.srt 16.5 KB
  15. Matplotlib 4.mp4 142.9 MB
  16. Development of Multivariate LSTM Model 4-en_US.srt 17.9 KB
  16. Development of Multivariate LSTM Model 4.mp4 169.1 MB
  16. Matplotlib 5-en_US.srt 13.8 KB
  16. Matplotlib 5.mp4 129.3 MB
  17. Development of Multivariate LSTM Model 5-en_US.srt 13.8 KB
  17. Development of Multivariate LSTM Model 5.mp4 131.4 MB
  2. ARIMA Model Development 1-en_US.srt 19 KB
  2. ARIMA Model Development 1.mp4 90.1 MB
  2. Introduction to Deep Learning - Activation Function-en_US.srt 7.6 KB
  2. Introduction to Deep Learning - Activation Function.mp4 20.1 MB
  2. NumPy 1-en_US.srt 7.9 KB
  2. NumPy 1.mp4 37.5 MB
  2. Python IDE Installation 1-en_US.srt 2.1 KB
  2. Python IDE Installation 1.mp4 4.8 MB
  2. Set Datetime as Index-en_US.srt 16.5 KB
  2. Set Datetime as Index.mp4 80.7 MB
  3. ARIMA Model Development 2-en_US.srt 14.4 KB
  3. ARIMA Model Development 2.mp4 91 MB
  3. Introduction to Deep Learning - How Neural Network Learn-en_US.srt 7.9 KB
  3. Introduction to Deep Learning - How Neural Network Learn.mp4 38.6 MB
  3. NumPy 2-en_US.srt 9.3 KB
  3. NumPy 2.mp4 56.9 MB
  3. Python IDE Installation 2-en_US.srt 5.4 KB
  3. Python IDE Installation 2.mp4 61.9 MB
  3. Resampling Method-en_US.srt 7.4 KB
  3. Resampling Method.mp4 42.2 MB
  4. ARIMA Model Development 3-en_US.srt 16.2 KB
  4. ARIMA Model Development 3.mp4 103.6 MB
  4. Introduction to Deep Learning - Optimization-en_US.srt 9.3 KB
  4. Introduction to Deep Learning - Optimization.mp4 29.7 MB
  4. NumPy 3-en_US.srt 13 KB
  4. NumPy 3.mp4 84.5 MB
  4. Python IDE Installation 3-en_US.srt 6.8 KB
  4. Python IDE Installation 3.mp4 24.5 MB
  5. Installing Required Libraries-en_US.srt 7.3 KB
  5. Installing Required Libraries.mp4 86.7 MB
  5. Introduction to Deep Learning - Recurrent Neural Network-en_US.srt 6.1 KB
  5. Introduction to Deep Learning - Recurrent Neural Network.mp4 23.7 MB
  5. Introduction to SARIMAX-en_US.srt 4.5 KB
  5. Introduction to SARIMAX.mp4 14.4 MB
  5. NumPy 4-en_US.srt 7.3 KB
  5. NumPy 4.mp4 56.5 MB
  6. Introduction to Deep Learning - LSTM Method-en_US.srt 13.9 KB
  6. Introduction to Deep Learning - LSTM Method.mp4 50 MB
  6. NumPy 5-en_US.srt 18.3 KB
  6. NumPy 5.mp4 152.6 MB
  6. SARIMAX Model Development 1-en_US.srt 11.9 KB
  6. SARIMAX Model Development 1.mp4 51.6 MB
  7. Development of Univariate LSTM Model 1-en_US.srt 17.6 KB
  7. Development of Univariate LSTM Model 1.mp4 64.3 MB
  7. NumPy 6-en_US.srt 17.7 KB
  7. NumPy 6.mp4 134.4 MB
  7. SARIMAX Model Development 2-en_US.srt 9.9 KB
  7. SARIMAX Model Development 2.mp4 64.5 MB
  8. Development of Univariate LSTM Model 2-en_US.srt 8 KB
  8. Development of Univariate LSTM Model 2.mp4 23.7 MB
  8. Pandas 1-en_US.srt 16.6 KB
  8. Pandas 1.mp4 95.6 MB
  8. SARIMAX Model Development 3-en_US.srt 14.5 KB
  8. SARIMAX Model Development 3.mp4 99.5 MB
  9. Development of Univariate LSTM Model 3-en_US.srt 13.2 KB
  9. Development of Univariate LSTM Model 3.mp4 66.1 MB
  9. Pandas 2-en_US.srt 16.6 KB
  9. Pandas 2.mp4 116.9 MB
  Bonus Resources.txt 307.2 B
  Data-Set.csv 3.4 KB
  Electricity-Consumption.csv 316.3 KB
  Get Bonus Downloads Here.url 204.8 B
  Pandas Date Frequencies.html 409.6 B
  Solar-Data-Set.csv 197.8 KB
  Source Codes.html 409.6 B
  Temp-Data.csv 15.9 KB
  ▲ 106 total files

Description


Time Series Analysis and Forecasting with Python



MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.99 GB | Duration: 10h 17m
What you'll learn
Basic Packages, NumPy, Pandas & Matplotlib
Time Series with Pandas (Creating Date Time index, Resampling, ...)
Analyzing Time Series Data Using Statsmodels Package
The Concept of ARIMA and SARIMAX method and How to Forecast into the Future Using Them
The Concept of Deep Learning from A-Z
Forecast into the Future Using LSTM Model for Single Variant
Forecast into the Future Using LSTM Model for Multi Variant
Requirements
General and Basic Python Skills
Description
"Time Series Analysis and Forecasting with Python" Course is an ultimate source for learning the concepts of Time Series and forecast into the future.

In this course, the most famous methods such as statistical methods (ARIMA and SARIMAX) and Deep Learning Method (LSTM) are explained in detail. Furthermore, several Real World projects are developed in a Python environment and have been explained line by line!

If you are a researcher, a student, a programmer, or a data science enthusiast that is seeking a course that shows you all about time series and prediction from A-Z, you are in a right place. Just check out what you will learn in this course below:

Basic libraries (NumPy, Pandas, Matplotlib)

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